Online Content Optimization with Hadoop__HadoopSummit2010
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1. The document discusses using Hadoop to optimize online content by ranking content from large pools for each user using different metrics like relevance, popularity, and affinity. 2. It describes how hundreds of gigabytes of data and metadata from user events are processed daily through Hadoop to build models for ranking millions of content items. 3. The system uses HBase for efficient storage and access of models, Pig and MapReduce for parallel processing, and Zookeeper for leader election and job coordination to optimize content across many contexts and user pools.
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Editor's Notes
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